PROJECT SUMMARY/ABSTRACT Chronic Kidney Disease affects 20 million people in the US and is associated with an approximately 3-5-fold increase in cardiovascular mortality. ESRD is the 9th leading cause of death in the US; it is associated with an approximately 20% yearly mortality rate, which is worse than most solid (colon, breast lung) cancers. Diabetic and hypertensive renal disease (DNP, HN) and FSGS -so called non- immune mediated degenerative glomerular diseases- are responsible for >75% of ESRD cases in the US. Currently, we have very few therapeutic options to offer to people with renal disease. In addition we have very limited tools to identify patients who are increased risk for the development of renal disease. In order to answer these questions we performed large scale gene expression studies using expression microarrays on human control and diseased kidney samples. We identified new gene expression patterns (potential biomarker patterns) that are associated with progression of renal disease. However, we found that the variation of gene expression levels is much higher in human samples than we previously observed in mouse. Thereby currently the expression levels of large number of genes are needed to identify patients with progressive renal disease. We propose that gene expression studies performed together with epigenomics analysis would facilitate the identification of new diagnostic and prognostic markers for progressive renal disease. 1.Generate genome scale cytosine methylation maps in control healthy and diseased kidney samples. We propose to use the HELP assay to determine DNA methylation patterns of microdissected glomerular and tubulointerstitial samples. 2. Compare DNA methylation profiles in the glomeruli and tubuli in control (healthy) and diseased kidney tissue samples and identify candidate methylation changes in kidney disease 3. Integrate results of mRNA expression and DNA methylation studies in order to identify a) new pathways, b) new biomarkers of progressive renal disease. The results of gene expression data that we already generated using Affymetrix expression arrays will be cross referenced and integrated with results of DNA methylation assays in order to identify new pathways and new biomarkers. In summary these studies would describe for the first time epigenetic modification in the kidney and help us to understand the complex regulatory network that leads to progressive loss of renal function. The availability of large number of well characterized human tissue samples with the corresponding gene expression data puts us into a unique position to achieve these goals.